Браузер не даёт сохранять данные (например, приватный режим): закладки пропадут после закрытия вкладки.
Поиск статей PubMed
Ключевые слова, автор, журнал, годы — результаты из PubMed, аннотации с переводом на русский, закладки в браузере.
Найдено: 4 925
Страница 185 из 493-
snRNA sequencing-based skeletal muscle analysis of Jiangquan black pigs with different average daily growth rates.
The Jiangquan black pigs, a new breed of swine obtained by introducing traits from Duroc pigs into Yimeng black pigs, exhibits fast growth rates and high meat quality. To understand how daily weight gain influences muscle development in this breed, we analyzed longissimus dorsi muscle cell subpopulations from Jiangquan black pigs using snRNA and bulk RNA sequencing. Thirteen distinct cell types (e.g., muscle stem cells, satellite cells, fibroblasts) were identified, and marker genes (PAX7, MYOD, MYOG) were found to exhibit stage-specific expression during differentiation. Pseudotime analysis revealed the differentiation trajectories of these cell populations, while cell cycle analysis uncovered the higher mitotic activity in satellite cells of the fast-growth versus slow-growth groups. Furthermore, cell communication analysis highlighted the interactions between muscle cells and other cell types. Finally, intergroup analysis revealed that 2,466 and 2,597 genes were differentially expressed in muscle stem cells and muscle satellite cells, respectively. These genes were enriched in disease-related pathways. This study provides a single-cell resolution atlas of porcine muscle development, offering insights into the genetic regulation of growth and potential targets for breeding optimization.
-
A Single-Cell Atlas of Large and Small Airways at Birth in a Porcine Model of Cystic Fibrosis.
Lack of CFTR (cystic fibrosis transmembrane conductance regulator) affects the transcriptome, composition, and function of large and small airway epithelia in people with advanced cystic fibrosis (CF); however, whether lack of CFTR causes cell-intrinsic abnormalities present at birth versus inflammation-dependent abnormalities is unclear. We performed a single-cell RNA-sequencing census of microdissected small airways from newborn CF pigs, which recapitulate CF host defense defects and pathology over time. Lack of CFTR minimally affected the transcriptome of large and small airways at birth, suggesting that infection and inflammation drive transcriptomic abnormalities in advanced CF. Importantly, common small airway epithelial cell types expressed a markedly different transcriptome than corresponding large airway cell types. Quantitative immunohistochemistry and electrophysiology of small airway epithelia demonstrated basal cells that reach the apical surface and a water and ion transport advantage. This single cell atlas highlights the archetypal nature of airway epithelial cells with location-dependent gene expression and function.
-
Establish TIIC signature score based the machine learning fusion in bladder cancer.
BACKGROUND: Bladder cancer is a prevalent malignant tumor with high heterogeneity. Current treatments, such as transurethral resection of bladder tumor (TURBT) and intravesical Bacillus Calmette-Guérin (BCG) therapy, still have limitations, with approximately 30% of non-muscle-invasive bladder cancer (NMIBC) progressing to muscle-invasive bladder cancer (MIBC), and a substantial number of MIBC patients experiencing recurrence after surgery. Immunotherapy has shown potential benefits, but accurate prediction of its prognostic effects remains challenging. METHODS: We analyzed bladder cancer RNA-seq data and clinical information from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and used various machine learning algorithms to screen for feature RNAs related to tumor-infiltrating immune cells (TIICs) from single-cell data. Based on these RNAs, we established a TIIC signature score and evaluated its relationship with overall survival (OS) and immunotherapy response in bladder cancer patients. RESULTS: The study identified 171 TIIC-RNAs and selected 11 TIIC-RNAs with prognostic value through survival analysis. The TIIC signature score established using a machine learning fusion method was significantly associated with OS and showed good predictive performance in different datasets. Additionally, the signature score was negatively correlated with immunotherapy response, with patients with low TIIC feature scores showing better survival outcomes after immunotherapy. Further biological functional analysis revealed a close association between the TIIC signature score and immune regulation processes, cellular metabolism, and genetic variations. CONCLUSION: This study successfully constructed and validated an RNA signature scoring system based on tumor-infiltrating immune cell (TIIC) features, which can effectively predict OS and the effectiveness of immunotherapy in bladder cancer patients.
-
Development and validation of a succinylation-related prognostic model for esophageal squamous cell carcinoma based on multi-omics bioinformatics analysis.
BACKGROUND: Esophageal squamous cell carcinoma (ESCC), an aggressive malignancy with poor prognosis, requires reliable prognostic biomarkers. Protein succinylation, a critical post-translational modification implicated in cancer biology, has not yet been systematically investigated for its prognostic significance in ESCC. METHODS: Transcriptomic data from GSE53624 (n = 119) and The Cancer Genome Atlas (TCGA)-ESCC (n = 95) cohorts were analyzed in this study. Succinylation-related genes (SRGs) were identified via weighted gene co-expression network analysis (WGCNA), a curated SRG set, and differential expression analysis. A prognostic signature was constructed using LASSO Cox regression and validated internally and externally. Immune infiltration was assessed by CIBERSORT/ssGSEA. miRNA-mRNA and protein-protein interactions (PPIs) and drug sensitivity were evaluated. Gene localization was confirmed with single-cell RNA sequencing (scRNA-seq). RESULTS: 41 high-confidence SRGs associated with ESCC were identified. A robust seven-gene prognostic signature (IGFBP3, CMA1, FN1, CTSG, TIMP1, MBL2, and SP5) was established. Patients stratified into high- and low-risk groups exhibited significantly different overall survival (OS) in both the training cohort (GSE53624, P < 0.001) and the validation cohort (TCGA, P = 0.026). The risk score remained an independent prognostic factor and was incorporated into a predictive nomogram. High-risk tumors were characterized by an immunosuppressive tumor microenvironment (TME), with reduced eosinophil and natural killer (NK) cell infiltration and increased monocyte abundance. In addition, signature genes were associated with resistance to multiple anticancer agents. scRNA-seq analysis revealed predominant expression of these genes in malignant cells, fibroblasts, and myeloid cells. CONCLUSION: A novel seven-gene succinylation-related signature was established and validated as an independent prognostic biomarker for ESCC. This signature captures immunosuppressive features of high-risk tumors and may provide a foundation for individualized therapeutic strategies.
-
Differences in maternal diet fiber content influence patterns of gene expression and chromatin accessibility in fetuses and piglets.
This study investigates the impact of maternal gestation diets with varying fiber contents on gene expression and chromatin accessibility in fetuses and piglets fed a low fiber diet post weaning. High-fiber maternal diets, enriched with sugar beet pulp or pea internal fiber, were compared to a low-fiber maternal diet to evaluate their effects on liver and muscle tissues. The findings demonstrate that maternal high-fiber diets significantly alter chromatin accessibility, predicted transcription factor activity and transcriptional landscape in both fetuses and piglets. A gene set enrichment analysis revealed over-expression of gene ontology terms related to metabolic processes and under-expression of those linked to immune responses in piglets from sows given the high-fiber diets during gestation. This suggests better metabolic health and immune tolerance of the fetus and offspring, in line with the documented epigenetic effects of short chain fatty acids on immune and metabolic pathways. A deconvolution analysis of the bulk RNA-seq data was performed using cell-type specific markers from a single cell transcriptome atlas of adult pigs. These results confirmed that the transcriptomic and chromatin accessibility data do not reflect different cell type compositions between maternal diet groups but rather phenotypic changes triggered by maternal nutrition in shaping the epigenetic and transcriptional environment of fetus and offspring. Our findings have implications for improving animal health and productivity as well as broader implications for human health, suggesting that optimizing maternal diet with high-fiber content could enhance metabolic health and immune function in the formative years after birth and potentially to adulthood.
-
A SUMOylation/immune-related gene signature predicts the prognosis and immunotherapy efficacy of patients with triple-negative breast cancer.
BACKGROUND: SUMOylation, a type of posttranslational protein modification, is linked to numerous biological processes, including tumorigenesis, the immunological response, and DNA repair. Immune-related genes are crucial for immune monitoring and the formation of the tumor immunological microenvironment. The aim of this work was to create a survival prediction model that combines SUMOylation and immune-related gene expression to guide personalized treatment strategies for triple-negative breast cancer (TNBC) patients. METHODS: RNA sequencing (RNA-seq) data and clinical information from TNBC patients were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). An unsupervised clustering method was applied to identify different SUMOylation-related subclusters in TNBC. Using immune cell infiltration levels and immunological scores, we performed weighted gene coexpression network analysis (WGCNA) to identify SUMOylated and immune-related genes (SIRGs). Three machine learning methods, namely, least absolute shrinkage and selection operator (LASSO), support vector machine recursive feature elimination (SVM-RFE), and random forest (RF), were utilized to construct a risk prognosis model from the SIRGs. The model genes were also analyzed through single-cell sequencing. Furthermore, we explored the correlations between risk score and patient prognosis, immune cell infiltration, and immunotherapy. The expression of characteristic genes was ultimately validated by quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry (IHC). RESULTS: The expression levels of genes (MITD1, IL12B, and ZP1) identified by machine learning were used to develop a prognostic model and nomogram. The low-risk patients had significantly better survival rates than the high-risk patients in both the training and validation cohorts. Specifically, patients in the low-risk group had increased immune cell infiltration, immune checkpoint gene expression, and sensitivity to immunotherapy. CONCLUSION: The constructed SIRG-related prognostic model can accurately predict prognosis and treatment efficacy for patients with TNBC.
-
Asian diversity in human immune cells.
The relationships of human diversity with biomedical phenotypes are pervasive yet remain understudied, particularly in a single-cell genomics context. Here, we present the Asian Immune Diversity Atlas (AIDA), a multi-national single-cell RNA sequencing (scRNA-seq) healthy reference atlas of human immune cells. AIDA comprises 1,265,624 circulating immune cells from 619 donors, spanning 7 population groups across 5 Asian countries, and 6 controls. Though population groups are frequently compared at the continental level, we found that sub-continental diversity, age, and sex pervasively impacted cellular and molecular properties of immune cells. These included differential abundance of cell neighborhoods as well as cell populations and genes relevant to disease risk, pathogenesis, and diagnostics. We discovered functional genetic variants influencing cell-type-specific gene expression, which were under-represented in non-Asian populations, and helped contextualize disease-associated variants. AIDA enables analyses of multi-ancestry disease datasets and facilitates the development of precision medicine efforts in Asia and beyond.
-
Leveraging multiple labeled datasets for the automated annotation of single-cell RNA and ATAC data.
The creation of single-cell atlases is essential for understanding cellular diversity and heterogeneity. However, assembling these atlases is challenging due to batch effects and the need for accurate and consistent cell annotation. Current methods for single-cell RNA and ATAC sequencing (scRNA-Seq and scATAC-Seq), while effective for integration, are not optimized for cell annotation. Additionally, many annotation tools rely on external databases or reference scRNA-Seq datasets, which may limit their adaptability to specific study needs, especially for rare cell-types or scATAC-Seq data. Here, we introduce JIND-Multi, a new framework designed to transfer cell-type labels across multiple annotated datasets. Notably, JIND-Multi can be applied to both scRNA-Seq and scATAC-Seq data, requiring in each case annotated data of the same type, contrary to most methods for scATAC-Seq data that require (paired) annotated scRNA-Seq data. In both cases, JIND-Multi significantly reduces the proportion of unclassified cells while maintaining the accuracy and performance of the original JIND model, and compares favorable to state-of-the-art methods. These results prove its versatility and effectiveness across different single-cell sequencing technologies. JIND-Multi represents an improvement in cell annotation, reducing unassigned cells and offering a reliable solution for both scRNA-Seq and scATAC-Seq data. Its ability to handle multiple labeled datasets enhances the precision of annotations, making it a valuable tool for the single-cell research community. JIND-Multi is publicly available at: https://github.com/ML4BM-Lab/JIND-Multi.git.
-
Oncolytic Virus Infection Modulates Lysine Acetyltransferase in Gliomas: Comprehensive Analysis and Experimental Validation of KAT8 in Glioma.
Oncolytic virotherapy, which uses engineered viruses to selectively target tumour cells, has emerged as a potential treatment in glioma. However, how oncolytic virus infection modulates lactylation enzymes in gliomas remains unclear. The RNA-seq data after oncolytic virus EV-A71 infection on glioma cells was analysed to screen and lysine acetyltransferase 8 (KAT8) was selected. We analysed KAT8 expression in glioma tissues using data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. Associations between KAT8 expression and clinicopathological features were evaluated. Kaplan-Meier survival analysis and Cox regression models were used to assess the prognostic value of KAT8. Functional annotation analyses were performed to explore the KAT8-related biological processes. Single-cell RNA-sequencing data were analysed to investigate cell-type-specific KAT8 expression. In vitro experiments were conducted to validate the effects of KAT8 knockdown on apoptosis in glioma cells. KAT8 was among the top 5 down-regulated genes in glioma cells after oncolytic viruses EV-A71 infection. KAT8 was significantly overexpressed in glioma tissues compared to normal brain tissues, with higher expression in lower-grade gliomas. High KAT8 expression was associated with 1p19q co-deletion, IDH mutation, and younger patient age. Notably, high KAT8 expression correlated with better overall survival in grade IV gliomas. Functional analyses revealed KAT8's involvement in neuronal development, mRNA processing, and apoptosis regulation. Single-cell sequencing data revealed a negative correlation between KAT8 expression and apoptosis in glioblastoma cells. In vitro experiments confirmed the increased apoptosis in glioma cell lines following KAT8 knockdown. Our findings suggest that the regulatory pathway of oncolytic virus infection for lactylation enzymes may be a key pathway affecting gliomas. These findings highlight KAT8 as a potential prognostic biomarker and therapeutic target for gliomas.
-
A novel in vitro model of primary human pediatric lung epithelial cells.
BACKGROUND: Current in vitro human lung epithelial cell models derived from adult tissues may not accurately represent all attributes that define homeostatic and disease mechanisms relevant to the pediatric lung. METHODS: We report methods for growing and differentiating primary Pediatric Human Lung Epithelial (PHLE) cells from organ donor infant lung tissues. We use immunohistochemistry, flow cytometry, quantitative RT-PCR, and single cell RNA sequencing (scRNAseq) analysis to characterize the cellular and transcriptional heterogeneity of PHLE cells. RESULTS: PHLE cells can be expanded in culture up to passage 6, with a doubling time of ~4 days, and retain attributes of highly enriched epithelial cells. PHLE cells can form resistant monolayers, and undergo differentiation when placed at air-liquid interface. When grown at Air-Liquid Interface (ALI), PHLE cells expressed markers of airway epithelial cell lineages. scRNAseq suggests the cultures contained 4 main sub-phenotypes defined by expression of FOXJ1, KRT5, MUC5B, and SFTPB. These cells are available to the research community through the Developing Lung Molecular Atlas Program Human Tissue Core. CONCLUSION: Our data demonstrate that PHLE cells provide a novel in vitro human cell model that represents the pediatric airway epithelium, which can be used to study perinatal developmental and pediatric disease mechanisms.